Qualitative and quantitative data in web analytics

Qualitative and quantitative data in web analytic


Having the ability to collect and interpret data is an incredibly important part of any process or method related to web analytics. To do this, the first thing to do is learn to distinguish between qualitative and quantitative data.

The typologies and groupings of the analysis of qualitative and quantitative data are very useful for the analyst to reach conclusions. They influence strategic business decisions and design processes.

In web analytics, the information that we obtain through digital channels is initially divided into two large groups.

Quantitative data

Quantitative data is data that comes in the form of numbers, amounts, percentages, proportions, or metrics. They are often used in the early stages of deployment when determining key performance indicators and metrics to sample. In this area, we are talking about the volume of data generated over time.

Thanks to this type of data, hypotheses can be established for tests and experiments. A/B and multivariate tests can also be used to extract information and reports of different kinds.

Why quantitative data is important.

Quantitative data is important as it helps us measure campaign performance, such as conversion rate, sessions, task completion, etc. It also helps to know how many users reach each stage of the customer journey to improve processes and achieve more conversions or sales.

Quantitative data is quick and inexpensive to collect, easy to analyze, and can be visualized in graphs, diagrams, and tables. They make it easy for interested parties to know the numbers associated with the business to make estimates. They make guesses to really understand the value they bring.

Having these references will help in the growth phases or conversion rate optimization since they highlight possible changes and improvements. The input of this data can be constant thanks to different analytical systems. Detecting opportunities is essential to justify actions by establishing priorities.

Collecting quantitative data

Quantitative data is typically collected by implementing tracking systems on websites and applications. The information is sent thanks to the cookies that are stored through the browser or through scripts included in the pages that connect with servers in the background.

Among many important information that we will have at our disposal are:

  • Behavioral analytical data such as visits, time spent or bounce rate.
  • Information associated with the different stages of the conversion funnel.
  • Estimated references based on specific metrics or more complete dashboards.

Qualitative data

Qualitative data is data that we learn to interpret and make sense of within its context. An example is opinions about user experience or behaviors that respond to cognitive biases.

The evaluations, positive or not, from the public or customers on the product pages of an ecommerce, are another example. This data is related to user satisfaction levels and their emotions. Numbers and metrics can also give away and reveal feelings.

Why qualitative data is important

Quality qualitative data is very important as it helps us understand the reason behind the behavior of a user. They can help paint a complete picture of the buyer persona and help us to empathize with him. We assess how you reason or think, what you feel and probably what you need.

We go to them to find out the preferences and tastes of groups of people and define segments focusing on more specific profiles. Well-segmented digital marketing actions, thanks to this data, are the ones that always achieve a higher percentage of success.

This qualitative quality data is used to understand user motivations or discover weaknesses and opportunities for improvement. There is no better way to satisfy demand than by knowing the consumer’s concerns, problems or needs. The number of clicks on certain pages or the level of depth when scrolling can confirm your degree of interest in the content.

Qualitative data categorization also helps us diagnose problems related to our products or services. With them, we will be able to know how our target audience perceives the value proposition. And most importantly, discover new pain points or perhaps elements of differentiation with respect to the competition. 

Collecting qualitative data

To collect qualitative data, it is best to prepare a suitable web page, framework or environment. It can be a user interface with forms that include text fields, checkboxes and radio buttons, specifically designed for the occasion.

It can also be an online survey, a newsletter that includes an external link or a module on the web that asks for ratings. Here are more examples:

  • Interfaces or question lists for collecting survey information.
  • User interviews through online or offline channels.
  • Usability testing to validate web designs or digital products.
  • Interfaces aimed at more specific focus groups to open the market.

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A good qualitative and quantitative database in web analytics is most useful when used together. Qualitative data such as online surveys help uncover problems and opportunities. Quantitative data can help validate points of view and confirm what we think.

The combination of different layers of data should serve specific business objectives. Measuring selected aspects or events based on hypotheses will always be better than measuring all kinds of elements without establishing criteria. Time management, methodologies and available resources to analyze play a decisive role. This include choosing the right tools.